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  <h1>Source code for torch_tensorrt._Input</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span>

<span class="kn">import</span> <span class="nn">torch</span>

<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>


<div class="viewcode-block" id="Input"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input">[docs]</a><span class="k">class</span> <span class="nc">Input</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Defines an input to a module in terms of expected shape, data type and tensor format.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        shape_mode (torch_tensorrt.Input._ShapeMode): Is input statically or dynamically shaped</span>
<span class="sd">        shape (Tuple or Dict): Either a single Tuple or a dict of tuples defining the input shape.</span>
<span class="sd">            Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form</span>
<span class="sd">            ``{</span>
<span class="sd">                &quot;min_shape&quot;: Tuple,</span>
<span class="sd">                &quot;opt_shape&quot;: Tuple,</span>
<span class="sd">                &quot;max_shape&quot;: Tuple</span>
<span class="sd">            }``</span>
<span class="sd">        dtype (torch_tensorrt.dtype): The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
<span class="sd">        format (torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">class</span> <span class="nc">_ShapeMode</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
        <span class="n">STATIC</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">DYNAMIC</span> <span class="o">=</span> <span class="mi">1</span>

    <span class="n">shape_mode</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1">#: (torch_tensorrt.Input._ShapeMode): Is input statically or dynamically shaped</span>
    <span class="n">shape</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1">#: (Tuple or Dict): Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form ``{ &quot;min_shape&quot;: Tuple, &quot;opt_shape&quot;: Tuple, &quot;max_shape&quot;: Tuple }``</span>
    <span class="n">dtype</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">unknown</span>
    <span class="p">)</span>  <span class="c1">#: The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
    <span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="nb">format</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">contiguous</span>
    <span class="p">)</span>  <span class="c1">#: The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>

    <span class="n">DOMAIN_OFFSET</span> <span class="o">=</span> <span class="mf">2.0</span>
    <span class="n">low_tensor_domain_incl</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">high_tensor_domain_excl</span> <span class="o">=</span> <span class="n">low_tensor_domain_incl</span> <span class="o">+</span> <span class="n">DOMAIN_OFFSET</span>
    <span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>

<div class="viewcode-block" id="Input.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;__init__ Method for torch_tensorrt.Input</span>

<span class="sd">        Input accepts one of a few construction patterns</span>

<span class="sd">        Args:</span>
<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>

<span class="sd">        Keyword Arguments:</span>
<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>
<span class="sd">            min_shape (Tuple or List, optional): Min size of input tensor&#39;s shape range</span>
<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
<span class="sd">            opt_shape (Tuple or List, optional): Opt size of input tensor&#39;s shape range</span>
<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
<span class="sd">            max_shape (Tuple or List, optional): Max size of input tensor&#39;s shape range</span>
<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
<span class="sd">            dtype (torch.dtype or torch_tensorrt.dtype): Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</span>
<span class="sd">            format (torch.memory_format or torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
<span class="sd">            tensor_domain (Tuple(float, float), optional): The domain of allowed values for the tensor, as interval notation: [tensor_domain[0], tensor_domain[1]).</span>
<span class="sd">                Note: Entering &quot;None&quot; (or not specifying) will set the bound to [0, 2)</span>

<span class="sd">        Examples:</span>
<span class="sd">            - Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</span>
<span class="sd">            - Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</span>
<span class="sd">            - Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
                <span class="p">)</span>
            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Found that both shape (as a positional argument), and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
                <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>

        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span>
                <span class="nb">all</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">])</span>
            <span class="p">):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Missing required arguments for class Input</span><span class="se">\n</span><span class="s2">Either shape or all three of min_shape, opt_shape, max_shape must be defined&quot;</span>
                <span class="p">)</span>
            <span class="k">elif</span> <span class="p">(</span><span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span>
                <span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
            <span class="p">):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Found that both shape, and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
                <span class="p">)</span>

            <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]))</span>
                    <span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">])</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]))</span>
                        <span class="o">+</span> <span class="s2">&quot; for min_shape&quot;</span>
                    <span class="p">)</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]))</span>
                        <span class="o">+</span> <span class="s2">&quot; for opt_shape&quot;</span>
                    <span class="p">)</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]))</span>
                        <span class="o">+</span> <span class="s2">&quot; for max_shape&quot;</span>
                    <span class="p">)</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">{</span>
                    <span class="s2">&quot;min_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]),</span>
                    <span class="s2">&quot;opt_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]),</span>
                    <span class="s2">&quot;max_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]),</span>
                <span class="p">}</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Unexpected number of positional arguments for class Input </span><span class="se">\n</span><span class="s2">    Found </span><span class="si">{}</span><span class="s2"> arguments, expected either zero or a single positional arguments&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                    <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
                <span class="p">)</span>
            <span class="p">)</span>

        <span class="k">if</span> <span class="s2">&quot;dtype&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">]</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_dtype</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">])</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_to_torch_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">True</span>

        <span class="k">if</span> <span class="s2">&quot;format&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">format</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_format</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;format&quot;</span><span class="p">])</span>

        <span class="k">if</span> <span class="s2">&quot;tensor_domain&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
            <span class="n">domain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;tensor_domain&quot;</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">domain</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_tensor_domain</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span></div>

    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
            <span class="k">return</span> <span class="s2">&quot;Input(shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">, domain=[</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">))&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
            <span class="p">)</span>
        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
            <span class="k">return</span> <span class="s2">&quot;Input(min_shape=</span><span class="si">{}</span><span class="s2">, opt_shape=</span><span class="si">{}</span><span class="s2">, max_shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">, domain=[</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">))&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unknown input shape mode&quot;</span><span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_supported_input_size_type</span><span class="p">(</span><span class="n">input_size</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">False</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_parse_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">long</span>
            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int32</span>
            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span>
            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span>
            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">bool</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                    <span class="s2">&quot;Provided an unsupported data type as an input data type (support: bool, int32, long, half, float), got: &quot;</span>
                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
                <span class="p">)</span>

        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
            <span class="k">return</span> <span class="n">dtype</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                <span class="s2">&quot;Input data type needs to be specified with a torch.dtype or a torch_tensorrt.dtype, got: &quot;</span>
                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span>
            <span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_to_torch_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">long</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">long</span>
        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int32</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span>
        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span>
        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">bool</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="c1"># Default torch_dtype used in FX path</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>

    <span class="k">def</span> <span class="nf">is_trt_dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">long</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_parse_format</span><span class="p">(</span><span class="nb">format</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">format</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">memory_format</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">format</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">contiguous</span>
            <span class="k">elif</span> <span class="nb">format</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">channels_last</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Provided an unsupported tensor format (support: NHCW/contiguous_format, NHWC/channel_last)&quot;</span>
                <span class="p">)</span>

        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">format</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="p">):</span>
            <span class="k">return</span> <span class="nb">format</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                <span class="s2">&quot;Tensor format needs to be specified with either torch.memory_format or torch_tensorrt.TensorFormat&quot;</span>
            <span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_parse_tensor_domain</span><span class="p">(</span><span class="n">domain</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a tuple of integers which specifies a tensor domain in the interval format: [lo, hi)</span>

<span class="sd">        Args:</span>
<span class="sd">            domain (Tuple[int, int]): A tuple of integers (or NoneTypes) to verify</span>

<span class="sd">        Returns:</span>
<span class="sd">            A tuple of two int32_t-valid integers</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">domain</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">result_domain</span> <span class="o">=</span> <span class="p">(</span>
                <span class="n">Input</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span><span class="p">,</span>
                <span class="n">Input</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span><span class="p">,</span>
            <span class="p">)</span>

        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">domain_lo</span><span class="p">,</span> <span class="n">domain_hi</span> <span class="o">=</span> <span class="n">domain</span>

            <span class="c1"># Validate type and provided values for domain</span>
            <span class="n">valid_type_lo</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
            <span class="n">valid_type_hi</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>

            <span class="k">if</span> <span class="ow">not</span> <span class="n">valid_type_lo</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Expected value for tensor domain low specifier, got </span><span class="si">{</span><span class="n">domain_lo</span><span class="si">}</span><span class="s2">&quot;</span>
                <span class="p">)</span>
            <span class="k">elif</span> <span class="ow">not</span> <span class="n">valid_type_hi</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Expected value for tensor domain high specifier, got </span><span class="si">{</span><span class="n">domain_hi</span><span class="si">}</span><span class="s2">&quot;</span>
                <span class="p">)</span>

            <span class="k">if</span> <span class="n">domain_hi</span> <span class="o">&lt;=</span> <span class="n">domain_lo</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Expected provided integer range to have low tensor domain value &quot;</span>
                    <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;&lt; high tensor domain value, got invalid range [</span><span class="si">{</span><span class="n">domain_lo</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">domain_hi</span><span class="si">}</span><span class="s2">)&quot;</span>
                <span class="p">)</span>
            <span class="n">result_domain</span> <span class="o">=</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Expected 2 values for domain, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">domain</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="p">)</span>

        <span class="k">return</span> <span class="n">result_domain</span>

<div class="viewcode-block" id="Input.from_tensor"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensor">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_tensor</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Input&quot;</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a Input which contains the information of the given PyTorch tensor.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensor (torch.Tensor): A PyTorch tensor.</span>

<span class="sd">        Returns:</span>
<span class="sd">            A Input object.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
            <span class="p">[</span>
                <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">),</span>
                <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">),</span>
            <span class="p">]</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Tensor does not have a supported memory format, supported formats are contiguous or channel_last&quot;</span>
            <span class="p">)</span>
        <span class="n">frmt</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span>
            <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
            <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="n">frmt</span><span class="p">)</span></div>

<div class="viewcode-block" id="Input.from_tensors"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensors">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_tensors</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">ts</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;Input&quot;</span><span class="p">]:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a list of Inputs which contain</span>
<span class="sd">        the information of all the given PyTorch tensors.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensors (Iterable[torch.Tensor]): A list of PyTorch tensors.</span>

<span class="sd">        Returns:</span>
<span class="sd">            A list of Inputs.</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
        <span class="k">return</span> <span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">ts</span><span class="p">]</span></div>

<div class="viewcode-block" id="Input.example_tensor"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.example_tensor">[docs]</a>    <span class="k">def</span> <span class="nf">example_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optimization_profile_field</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Get an example tensor of the shape specified by the Input object</span>

<span class="sd">        Args:</span>
<span class="sd">            optimization_profile_field (Optional(str)): Name of the field to use for shape in the case the Input is dynamically shaped</span>

<span class="sd">        Returns:</span>
<span class="sd">            A PyTorch Tensor</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="k">assert</span> <span class="nb">any</span><span class="p">(</span>
                    <span class="p">[</span>
                        <span class="n">optimization_profile_field</span> <span class="o">==</span> <span class="n">field_name</span>
                        <span class="k">for</span> <span class="n">field_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
                    <span class="p">]</span>
                <span class="p">)</span>
            <span class="k">except</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Invalid field name, expected one of min_shape, opt_shape, max_shape&quot;</span>
                <span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span>
            <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Specified a optimization profile field but the input is static&quot;</span>
            <span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span>
            <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="kc">None</span>
            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Requested an example tensor from a dynamic shaped input but did not specific which profile field to use.&quot;</span>
            <span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">optimization_profile_field</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
                <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span>
            <span class="p">)</span></div></div>
</pre></div>

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